Search Results (3)

This paper presents a novel model for predicting human movements and introduces a new control method for human–robot interaction based on this model. The developed predictive model of human movement is a holistic model that is based on well-supported neuroscientific and biomechanical theories of human motor control; it includes multiple levels of the human sensorimotor system hierarchy, including high-level decision-making based on internal models, muscle synergies, and physiological muscle mechanics. Therefore, this holistic model can predict arm kinematics and neuromuscular activities in a computationally efficient way. The computational efficiency of the model also makes it suitable for repetitive predictive simulations within a robot’s control algorithm to predict the user’s behavior in human–robot interactions. Therefore, based on this model and the nonlinear model predictive control framework, a human-aware control algorithm is implemented, which internally runs simulations to predict the user’s interactive movement patterns in the future. Consequently, it can optimize the robot’s motor torques to minimize an index, such as the user’s neuromuscular effort. Simulation results of the holistic model and its utilization in the human-aware control of a two-link robot arm are presented. The holistic model is shown to replicate salient features of human movements. The human-aware controller’s ability to predict and minimize the user’s neuromuscular effort in a collaborative task is also demonstrated in simulations. Full article

Neurodiversity is a concept and a social movement that addresses and normalizes human neurocognitive heterogeneity to promote acceptance and inclusion of neuro-minorities (e.g., learning disabilities, attention disorders, psychiatric disorders, and more) in contemporary society. Neurodiversity is attributed to nature and nurture factors, and about a fifth of the human population is considered neurodivergent. What does neurodiversity mean neuroscientifically? This question forms the foundation of the present entry, which focuses on existing scientific evidence on neurodiversity including neurodiversity between and within individuals, and the evolutional perspective of neurodiversity. Furthermore, the neuroscientific view will be synergistically integrated with social approaches, particularly in the context of the normalization of neurodiversity and its association with the medical and social models of disability. This multidimensional analysis offers a cohesive and comprehensive understanding of neurodiversity, drawing insights from various vantage points, such as social, psychological, clinical, and neuroscientific viewpoints. This integrated approach fosters a nuanced and holistic discussion on the topic of human diversity. Full article

The classification of different fine hand movements from electroencephalogram (EEG) signals represents a relevant research challenge, e.g., in BCI applications for motor rehabilitation. Here, we analyzed two different datasets where fine hand movements (touch, grasp, palmar, and lateral grasp) were performed in a self-paced modality. We trained and tested a newly proposed CNN, and we compared its classification performance with two well-established machine learning models, namely, shrinkage-linear discriminant analysis (LDA) and Random Forest (RF). Compared to previous literature, we included neuroscientific evidence, and we trained our Convolutional Neural Network (CNN) model on the so-called movement-related cortical potentials (MRCPs). They are EEG amplitude modulations at low frequencies, i.e., $(0.3,3)$ Hz that have been proved to encode several properties of the movements, e.g., type of grasp, force level, and speed. We showed that CNN achieved good performance in both datasets (accuracy of $0.70\pm 0.11$ and $0.64\pm 0.10$, for the two datasets, respectively), and they were similar or superior to the baseline models (accuracy of $0.68\pm 0.10$ and $0.62\pm 0.07$ with sLDA; accuracy of $0.70\pm 0.15$ and $0.61\pm 0.07$ with RF, with comparable performance in precision and recall). In addition, compared to the baseline, our CNN requires a faster pre-processing procedure, paving the way for its possible use in online BCI applications. Full article